Statistics and data analysis for financial engineering : with R examples /
By: Ruppert, David
Title By: Matteson, David S
Material type: BookSeries: Springer texts in statisticsPublisher: New York : Springer, c2015.Edition: 2nd ed.Description: xxvi, 719 p. : ill. ; 25 cm.ISBN: 978-1493926138Subject(s): Financial engineering -- Statistical methods | Finance -- Statistical methods | Quantitative Finance | Statistical Theory and Methods | Financial EngineeringDDC classification: . Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
REGULAR | University of Wollongong in Dubai Main Collection | 658.15 RU ST (Browse shelf) | Available | T0018145 |
Includes bibliographical references and index.
Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines.
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.